Object recognition is enabling innovative systems like self-driving cars, image based retrieval, and autonomous robotics. The machine learning and deep learning these systems rely on can be difficult to train, evaluate, and compare.

In this webinar we explore how MATLAB addresses the most common challenges encountered while developing object recognition systems. This webinar will cover new capabilities for deep learning, machine learning and computer vision.

We will use real-world examples to demonstrate:

Training models using large image datasets

Training deep neural networks from scratch

Using transfer learning to re-use trained deep networks for new tasks

Exploring the tradeoffs between machine learning and deep learning

About the Presenters

Johanna Pingel joined the MathWorks team in 2013, specializing in Image Processing and Computer Vision applications with MATLAB. She has a M.S. degree from Rensselaer Polytechnic Institute and a B.A. degree from Carnegie Mellon University. She has been working in the Computer Vision application space for over 5 years, with a focus on object detection and tracking.

Avinash Nehemiah works on computer vision applications in technical marketing at MathWorks. Prior to joining MathWorks he spent 7 years as an algorithm developer and researcher designing computer vision algorithms for hospital safety and video surveillance. He holds an MSEE degree from Carnegie Mellon University.